System and Method for Adaptable Trend Detection for Component Condition Indicator Data

ABSTRACT

A system for adaptable trend detection for component condition indicator data includes a sensor operable to measure an operating condition of a vehicle and generate a sensor signal associated with the operating condition and a data server operable to acquire a current condition indicator of a condition indicator set according to the sensor signal, and to determine whether a trend in the condition indicator set is indicated according to at least the current condition indicator, at least one previous condition indicator of the condition indicator set and a volatility of at least a portion of the condition indicator set. The data server is further operable to provide an alert in response to determining that the trend is indicated.

TECHNICAL FIELD

The present invention relates generally to a system and method forsystem degradation or failure and maintenance analysis, and, inparticular embodiments, to a system and method for detecting trends incondition indicators for vehicles.

BACKGROUND

A rotorcraft may include one or more rotor systems including one or moremain rotor systems. A main rotor system generates aerodynamic lift tosupport the weight of the rotorcraft in flight and thrust to move therotorcraft in forward flight. Another example of a rotorcraft rotorsystem is a tail rotor system. A tail rotor system may generate thrustin the same direction as the main rotor system's rotation to counter thetorque effect created by the main rotor system. For smooth and efficientflight in a rotorcraft, a pilot balances the engine power, main rotorcollective thrust, main rotor cyclic thrust and the tail rotor thrust,and a control system may assist the pilot in stabilizing the rotorcraftand reducing pilot workload. The systems for engines, transmissions,drive system, rotors, and the like, are critical to the safe operationof the rotorcraft in flight. The elements of system such as mechanicalsystems, electrical systems, hydraulic systems, and the like, are eachsubject to unique wear factors and monitoring, inspection or maintenancerequirements.

SUMMARY

An embodiment system includes a sensor operable to measure an operatingcondition of a vehicle and generate a sensor signal associated with theoperating condition and a data server operable to acquire a currentcondition indicator of a condition indicator set according to the sensorsignal, and to determine whether a trend in the condition indicator setis indicated according to at least the current condition indicator, atleast one previous condition indicator of the condition indicator setand a volatility of at least a portion of the condition indicator set.The data server is further operable to provide an alert in response todetermining that the trend is indicated.

An embodiment data server includes a processor and a non-transitorycomputer-readable storage medium storing a program to be executed by theprocessor. The program including instructions for acquiring a currentcondition indicator of a condition indicator set associated with anoperating condition of a vehicle, with the condition indicator setindicating sensor readings associated with an operating element of thevehicle under the operating condition, and determining a volatility of afirst portion of the condition indicator set, where the first portion ofthe condition indicator set includes the current condition indicator.The program further includes instructions for determining one or moremoving averages of a second portion of the condition indicator set,determining whether a trend associated with the operating element isindicated according to the one or more moving averages and thevolatility, and generating an alert signal in response to thedetermining that the trend is indicated.

An embodiment method includes acquiring a current condition indicator ofa condition indicator set associated with an operating condition of avehicle, with the condition indicator set indicating sensor readingsassociated with an operating element of the vehicle under the operatingcondition, determining, by a data server, a volatility of a firstportion of the condition indicator set, where the first portion of thecondition indicator set includes the current condition indicator,determining, by the data server, one or more moving averages of a secondportion of the condition indicator set, determining, by the data server,whether a trend associated with the operating element is indicatedaccording to the one or more moving averages and the volatility, andgenerating, by the data server, an alert signal in response to thedetermining that the trend is indicated.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1 illustrates a rotorcraft according to some embodiments;

FIG. 2 illustrates a fly-by-wire flight control system for a rotorcraftaccording to some embodiments;

FIG. 3 is a block diagram illustrating a system for trend detection ofcomponent condition indicator data according to some embodiments;

FIG. 4A is a graph illustrating the calculation of exponential movingaverage (EMA) values and moving average convergence divergence (MACD)values 409 according to an embodiment;

FIG. 4B is a graph illustrating condition indicator data 415 and a trendindication according to some embodiments.

FIG. 5A is a graph illustrating a trend indication using the statictrend threshold 407 with noisy condition indicator data according tosome embodiments;

FIG. 5B is a chart illustrating the calculation of EMA values and MACDvalues in comparison to a static trend threshold according to someembodiments;

FIG. 5C is a chart illustrating the calculation of EMA values and MACDvalues in comparison to an adaptable trend threshold according to someembodiments;

FIG. 5D is a chart illustrating the calculation of EMA values and MACDvalues in comparison to an adaptable trend threshold according to someembodiments;

FIG. 6A is a graph illustrating condition indicator data and a trendindication according to some embodiments;

FIG. 6B is a graph illustrating the calculation of EMA values andadaptable trend threshold according to some embodiments;

FIG. 7A is a graph illustrating condition indicator data and a trendindication according to some embodiments;

FIG. 7B is a graph illustrating the calculation of EMA values andadaptable trend threshold according to some embodiments;

FIG. 8A is a graph illustrating condition indicator data and a trendindication according to some embodiments;

FIG. 8B is a graph illustrating the calculation of EMA values andadaptable trend threshold according to some embodiments;

FIG. 8C is a graph illustrating a variety of scaling functions accordingto some embodiments;

FIG. 9A is a flow diagram illustrating a method for providing an alertsignal for condition indicator trends according to some embodiments;

FIG. 9B is a flow diagram illustrating a method for providing an alertsignal for quickly changing and slowly changing condition indicatortrends according to some embodiments; and

FIG. 10 is a diagram illustrating a computer system that may be used toimplement a system, data terminal, or data server according to someembodiments.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Illustrative embodiments of the system and method of the presentdisclosure are described below. In the interest of clarity, all featuresof an actual implementation may not be described in this specification.It will of course be appreciated that in the development of any suchactual embodiment, numerous implementation-specific decisions may bemade to achieve the developer's specific goals, such as compliance withsystem-related and business-related constraints, which will vary fromone implementation to another. Moreover, it should be appreciated thatsuch a development effort might be complex and time-consuming but wouldnevertheless be a routine undertaking for those of ordinary skill in theart having the benefit of this disclosure.

Reference may be made herein to the spatial relationships betweenvarious components and to the spatial orientation of various aspects ofcomponents as the devices are depicted in the attached drawings.However, as will be recognized by those skilled in the art after acomplete reading of the present disclosure, the devices, members,apparatuses, etc. described herein may be positioned in any desiredorientation. Thus, the use of terms such as “above,” “below,” “upper,”“lower,” or other like terms to describe a spatial relationship betweenvarious components or to describe the spatial orientation of aspects ofsuch components should be understood to describe a relative relationshipbetween the components or a spatial orientation of aspects of suchcomponents, respectively, as the device described herein may be orientedin any desired direction.

The increasing use of rotorcraft, in particular, for commercial andindustrial applications, has led to the development of larger morecomplex rotorcraft. However, as rotorcraft become larger and morecomplex, the differences between flying rotorcraft and fixed wingaircraft has become more pronounced. Since rotorcraft use one or moremain rotors to simultaneously provide lift, control attitude, controlaltitude, and provide lateral or positional movement, different flightparameters and controls are tightly coupled to each other, as theaerodynamic characteristics of the main rotors affect each control andmovement axis. For example, the flight characteristics of a rotorcraftat cruising speed or high speed may be significantly different than theflight characteristics at hover or at relatively low speeds.Additionally, different flight control inputs for different axes on themain rotor, such as cyclic inputs or collective inputs, affect otherflight controls or flight characteristics of the rotorcraft. Forexample, pitching the nose of a rotorcraft forward to increase forwardspeed will generally cause the rotorcraft to lose altitude. In such asituation, the collective may be increased to maintain level flight, butthe increase in collective requires increased power at the main rotorwhich, in turn, requires additional anti-torque force from the tailrotor. This is in contrast to fixed wing systems where the controlinputs are less closely tied to each other and flight characteristics indifferent speed regimes are more closely related to each other.

Recently, fly-by-wire (FBW) systems have been introduced in rotorcraftto assist pilots in stably flying the rotorcraft and to reduce workloadon the pilots. The FBW system may provide different controlcharacteristics or responses for cyclic, pedal or collective controlinput in the different flight regimes, and may provide stabilityassistance or enhancement by decoupling physical flight characteristicsso that a pilot is relieved from needing to compensate for some flightcommands issued to the rotorcraft. FBW systems may be implemented in oneor more flight control computers (FCCs) disposed between the pilotcontrols and flight control systems, providing corrections to flightcontrols that assist in operating the rotorcraft more efficiently orthat put the rotorcraft into a stable flight mode while still allowingthe pilot to override the FBW control inputs. The FBW systems in arotorcraft may, for example, automatically adjust power output by theengine to match a collective control input, apply collective or powercorrection during a cyclic control input, provide automation of one ormore flight control procedures provide for default or suggested controlpositioning, or the like.

Embodiments of the system presented herein are directed to providing asystem for measuring operating conditions in a vehicle, and determiningtrends of the operating conditions in order to provide condition alertsto operators and technicians. In some embodiments, a reporting systemreceives signals from one or more sensors, and determines conditionindicators that may indicate the magnitude or other properties ofconditions such as vibration or the like. The system analyzes a seriesof condition indicators associated with a particular operating elementto determine trends in the condition indicators. The trends may be usedto determine that the condition indicators for a particular operatingelement has changed due to, for example, wear, damage, or the like. Forexample, vibration associated with a fuel pump, transmission gear,engine turbine, or the like, may be tracked, and trends associated witha change outside of a trend threshold may indicate that the operatingelement may need inspection, repair or replacement.

In some embodiments, the system may use a moving average of conditionindicators, such as an exponential moving average (EMA), to determinetrends for the associated operating element. In some embodiments, movingaverage convergence divergence (MACD) analysis may be used to determinethe trends, and in some embodiments, the MACD may be a differencebetween a short term EMA and a longer term EMA. The MACD may be comparedto a static trend threshold, or to an adaptable trend threshold based onone or more of the data or condition indicators, the volatility of thecondition indicators, the static trend threshold, or the like, and thecomparison may indicate the trend. When a trend is detected, the systemmay provide an alert through, for example, an indicator in the vehicle,through a report, through a web interface accessible through a server,through an automated message, or the like.

FIG. 1 illustrates a rotorcraft 101 according to some embodiments. Therotorcraft 101 has a main rotor system 103, which includes a pluralityof main rotor blades 105. The pitch of each main rotor blade 105 may becontrolled by a swashplate 107 in order to selectively control theattitude, altitude and movement of the rotorcraft 101. The swashplate107 may be used to collectively and/or cyclically change the pitch ofthe main rotor blades 105. The rotorcraft 101 also has an anti-torquesystem, which may include a tail rotor 109, no-tail-rotor (NOTAR), ordual main rotor system. In rotorcraft with a tail rotor 109, the pitchof each tail rotor blade 111 is collectively changed in order to varythrust of the anti-torque system, providing directional control of therotorcraft 101. The pitch of the tail rotor blades 111 is changed by oneor more tail rotor actuators. In some embodiments, the FBW system sendselectrical signals to the tail rotor actuators or main rotor actuatorsto control flight of the rotorcraft.

Power is supplied to the main rotor system 103 and the anti-torquesystem by engines 115. There may be one or more engines 115, which maybe controlled according to signals from the FBW system. The output ofthe engine 115 is provided to a driveshaft 117, which is mechanicallyand operatively coupled to the rotor system 103 and the anti-torquesystem through a main rotor transmission 119 and a tail rotortransmission, respectively.

The rotorcraft 101 further includes a fuselage 125 and tail section 123.The tail section 123 may have other flight control devices such ashorizontal or vertical stabilizers, rudder, elevators, or other controlor stabilizing surfaces that are used to control or stabilize flight ofthe rotorcraft 101. The fuselage 125 includes a cockpit 127, whichincludes displays, controls, and instruments. It should be appreciatedthat even though rotorcraft 101 is depicted as having certainillustrated features, the rotorcraft 101 may have a variety ofimplementation-specific configurations. For instance, in someembodiments, cockpit 127 is configured to accommodate a pilot or a pilotand co-pilot, as illustrated. It is also contemplated, however, thatrotorcraft 101 may be operated remotely, in which case cockpit 127 couldbe configured as a fully functioning cockpit to accommodate a pilot (andpossibly a co-pilot as well) to provide for greater flexibility of use,or could be configured with a cockpit having limited functionality(e.g., a cockpit with accommodations for only one person who wouldfunction as the pilot operating perhaps with a remote co-pilot or whowould function as a co-pilot or back-up pilot with the primary pilotingfunctions being performed remotely. In yet other contemplatedembodiments, rotorcraft 101 could be configured as an unmanned vehicle,in which case cockpit 127 could be eliminated entirely in order to savespace and cost.

FIG. 2 illustrates a fly-by-wire flight control system 201 for arotorcraft according to some embodiments. A pilot may manipulate one ormore pilot flight controls in order to control flight of the rotorcraft.The pilot flight controls may include manual controls such as a cyclicstick 231 in a cyclic control assembly 217, a collective stick 233 in acollective control assembly 219, and pedals 239 in a pedal controlassembly 221. Inputs provided by the pilot to the pilot flight controlsmay be transmitted mechanically and/or electronically (e.g., via the FBWflight control system) to flight control devices by the flight controlsystem 201. Flight control devices may represent devices operable tochange the flight characteristics of the rotorcraft. Flight controldevices on the rotorcraft may include mechanical and/or electricalsystems operable to change the positions or angle of attack of the mainrotor blades 105 and the tail rotor blades in or to change the poweroutput of the engines 115, as examples. Flight control devices includesystems such as the swashplate 107, tail rotor actuator 113, and systemsoperable to control the engines 115. The flight control system 201 mayadjust the flight control devices independently of the flight crew inorder to stabilize the rotorcraft, reduce workload of the flight crew,and the like. The flight control system 201 includes engine controlcomputers (ECCUs) 203, flight control computers (FCCs) 205, and aircraftsensors 207, which collectively adjust the flight control devices andmonitors the rotorcraft during operation.

The flight control system 201 has one or more FCCs 205. In someembodiments, multiple FCCs 205 are provided for redundancy. One or moremodules within the FCCs 205 may be partially or wholly embodied assoftware and/or hardware for performing any functionality describedherein. In embodiments where the flight control system 201 is a FBWflight control system, the FCCs 205 may analyze pilot inputs anddispatch corresponding commands to the ECCUs 203, the tail rotoractuator 113, and/or actuators for the swashplate 107. Further, the FCCs205 are configured and receive input commands from the pilot controlsthrough sensors associated with each of the pilot flight controls. Theinput commands are received by measuring the positions of the pilotcontrols. The FCCs 205 also control tactile cues to the pilot controlsor display information in instruments on, for example, an instrumentpanel 241.

The ECCUs 203 control the engines 115. For example, the ECCUs 203 mayvary the output power of the engines 115 to control the rotational speedof the main rotor blades or the tail rotor blades. The ECCUs 203 maycontrol the output power of the engines 115 according to commands fromthe FCCs 205, or may do so based on feedback such as measuredrevolutions per minute (RPM) of the main rotor blades.

The cyclic control assembly 217 is connected to a cyclic trim assembly229 having one or more cyclic position sensors 211, one or more cyclicdetent sensors 235, and one or more cyclic actuators or cyclic trimmotors 209. The cyclic position sensors 211 measure the position of thecyclic stick 231. In some embodiments, the cyclic stick 231 is a singlecontrol stick that moves along two axes and permits a pilot to controlpitch, which is the vertical angle of the nose of the rotorcraft androll, which is the side-to-side angle of the rotorcraft. In someembodiments, the cyclic control assembly 217 has separate cyclicposition sensors 211 that measuring roll and pitch separately. Thecyclic position sensors 211 for detecting roll and pitch generate rolland pitch signals, respectively, (sometimes referred to as cycliclongitude and cyclic latitude signals, respectively) which are sent tothe FCCs 205, which controls the swashplate 107, engines 115, tail rotor109 or related flight control devices.

The cyclic trim motors 209 are connected to the FCCs 205, and receivesignals from the FCCs 205 to move the cyclic stick 231. In someembodiments, the FCCs 205 determine a suggested cyclic stick positionfor the cyclic stick 231 according to one or more of the collectivestick position, the pedal position, the speed, altitude and attitude ofthe rotorcraft, the engine RPM, engine temperature, main rotor RPM,engine torque or other rotorcraft system conditions or flightconditions, or according to a predetermined function selected by thepilot. The suggested cyclic stick position is a position determined bythe FCCs 205 to give a desired cyclic action. In some embodiments, theFCCs 205 send a suggested cyclic stick position signal indicating thesuggested cyclic stick position to the cyclic trim motors 209. While theFCCs 205 may command the cyclic trim motors 209 to move the cyclic stick231 to a particular position (which would in turn drive actuatorsassociated with swashplate 107 accordingly), the cyclic position sensors211 detect the actual position of the cyclic stick 231 that is set bythe cyclic trim motors 206 or input by the pilot, allowing the pilot tooverride the suggested cyclic stick position. The cyclic trim motor 209is connected to the cyclic stick 231 so that the pilot may move thecyclic stick 231 while the trim motor is driving the cyclic stick 231 tooverride the suggested cyclic stick position. Thus, in some embodiments,the FCCs 205 receive a signal from the cyclic position sensors 211indicating the actual cyclic stick position, and do not rely on thesuggested cyclic stick position to command the swashplate 107.

Similar to the cyclic control assembly 217, the collective controlassembly 219 is connected to a collective trim assembly 225 having oneor more collective position sensors 215, one or more collective detentsensors 237, and one or more collective actuators or collective trimmotors 213. The collective position sensors 215 measure the position ofa collective stick 233 in the collective control assembly 219. In someembodiments, the collective stick 233 is a single control stick thatmoves along a single axis or with a lever type action. A collectiveposition sensor 215 detects the position of the collective stick 233 andsends a collective position signal to the FCCs 205, which controlsengines 115, swashplate actuators, or related flight control devicesaccording to the collective position signal to control the verticalmovement of the rotorcraft. In some embodiments, the FCCs 205 may send apower command signal to the ECCUs 203 and a collective command signal tothe main rotor or swashplate actuators so that the angle of attack ofthe main blades is raised or lowered collectively, and the engine poweris set to provide the needed power to keep the main rotor RPMsubstantially constant.

The collective trim motor 213 is connected to the FCCs 205, and receivessignals from the FCCs 205 to move the collective stick 233. Similar tothe determination of the suggested cyclic stick position, in someembodiments, the FCCs 205 determine a suggested collective stickposition for the collective stick 233 according to one or more of thecyclic stick position, the pedal position, the speed, altitude andattitude of the rotorcraft, the engine RPM, engine temperature, mainrotor RPM, engine torque or other rotorcraft system conditions or flightconditions, or according to a predetermined function selected by thepilot. The FCCs 205 generate the suggested collective stick position andsend a corresponding suggested collective stick signal to the collectivetrim motors 213 to move the collective stick 233 to a particularposition. The collective position sensors 215 detect the actual positionof the collective stick 233 that is set by the collective trim motor 213or input by the pilot, allowing the pilot to override the suggestedcollective stick position.

The pedal control assembly 221 has one or more pedal sensors 227 thatmeasure the position of pedals or other input elements in the pedalcontrol assembly 221. In some embodiments, the pedal control assembly221 is free of a trim motor or actuator, and may have a mechanicalreturn element that centers the pedals when the pilot releases thepedals. In other embodiments, the pedal control assembly 221 has one ormore trim motors that drive the pedal to a suggested pedal positionaccording to a signal from the FCCs 205. The pedal sensor 227 detectsthe position of the pedals 239 and sends a pedal position signal to theFCCs 205, which controls the tail rotor 109 to cause the rotorcraft toyaw or rotate around a vertical axis.

The cyclic and collective trim motors 209 and 213 may drive the cyclicstick 231 and collective stick 233, respectively, to suggestedpositions. The cyclic and collective trim motors 209 and 213 may drivethe cyclic stick 231 and collective stick 233, respectively, tosuggested positions, but this movement capability may also be used toprovide tactile cueing to a pilot. Additionally, the cyclic controlassembly 217, collective control assembly 219 and/or pedal controlassembly 221 may each have one or more detent sensors that determinewhether the pilot is handling a particular control device. The FCCs 205may provide different default control or automated commands to one ormore flight systems based on the detent status of a particular stick orpilot control.

The aircraft sensors 207 may be in communication with the FCCs 205, ahealth and usage monitoring system (HUMS) 245. The aircraft sensors 207may include sensors for monitoring operation of the rotorcraft,providing pilot data, providing operational data, or the like, and mayinclude measuring a variety of rotorcraft systems, operating conditions,flight parameters, environmental conditions and the like. For example,the aircraft sensors 207 may include sensors for gathering flight data,and may include sensors for measuring airspeed, altitude, attitude,position, orientation, temperature, airspeed, vertical speed, and thelike. The aircraft sensors 207 may include sensors relying upon data orsignals originating external to the rotorcraft, such as a globalpositioning system (GPS) sensor, a very high frequency (VHF)omnidirectional range sensor, Instrument Landing System (ILS), and thelike. The aircraft sensors 207 may also include sensors for readingoperational data such as vibration, device rotational speed, electricaloperating characteristics, fluid flows, or the like.

The flight control system 201 may further include the HUMS 245 or a HUMSterminal. In some embodiments, the HUMS collects data from flight system201 elements for storage and later download, analysis, or the like. Insome embodiments, the HUMS 245 may be connected to one or more aircraftsensors 207, FCCs 205, ECCUs 203, standalone sensors, sensors integratedinto the HUMs, or other system components, or a combination ofcomponents. In some embodiments, the HUMS 245 may be separate from theFCCs 205, and may be implemented as a standalone system thatcommunicates with, but that is operationally separate from, otherelements of the flight control system 201. The HUMS 245 may be aterminal that stores raw data from one or more aircraft components, andprovides the raw data to a server for interpretation and analysis. Inother embodiments, the HUMS 245 may interpret raw data to determine oneor more condition indicators for a server or other system that analyzesor displays the data. In yet another embodiment, the HUMS 245 mayanalyze the raw data or condition indicators to determine a trend orproblem with a data set, and may display or indicate the interpreteddata, a warning, a system status, or like, on the instrument panel 241,on a dedicated display, through an audible warning, within anotherdisplay such as a flight director display, though a tactile feedbacksystem, or the like. The HUMS 245 may use data from the aircraft sensor207 to determine an operating condition such as vibration. For example,the HUMS 245 may use a combination of vibration data and rotationalspeed data to generate synchronous vibration data or other transformeddata types, which may be analyzed for trends indicating developingproblems with specific components associated with the vibration data.

FIG. 3 is a block diagram illustrating a HUMS 301 for trend detection ofcomponent condition indicator data according to some embodiments. TheHUMS 301 may include a data terminal 303 that is connected to one ormore sensors 307 and a data server 305. The sensors 307 may be aircraftsensors, as discussed above, and may take sensor readings and generateone or more sensor signals such as electrical signals, data elements, orthe like, that indicate one or more operational conditions. For example,a sensor 307 may be a vibration sensor near an engine, gear set ortransmission that detects vibrations from the local operating elements.In another example, the sensor 307 may be a voltage sensor or currentdetector that detects the voltage or current drawn by an electricaloperating element such as a pump or a motor. In yet another example, thesensor 307 may be a pressure or flow sensor that detects the pressure ofa hydraulic line or fuel line, the flow rate of a fluid such as fuel,coolant, oil hydraulic fluid, or the like.

The data terminal 303 may be a computer or other device that receivesthe sensor signals and stores the sensor signals locally for lateranalysis. In some embodiments, one or more of the HUMS terminal, theFCCs or ECCUs are data terminals 303. The data terminal 303 has a datacollection element 309 that is a data handling element such as aprocessor, data collection circuit or device, or the like. In someembodiments, the data terminal 303 is a HUMS terminal that is acentralized device or standalone device that collects raw data from thesensors 307 and that generates condition indicator data from the rawdata signals, or that collects condition indicator data from the sensors307. In other embodiments, the data terminal 303 is a HUMS terminal thatreceives calculated or analyzed data such as alerts, trends, or the likefrom smart sensors 307 that determine condition indicators and performanalysis on the condition indicators. In yet another embodiment, thedata terminal 303 may be, or include, a network of smart sensors 307that act autonomously to collect data, and may determine conditionindicators and perform some analysis of the condition indicator data. Insuch an embodiment, the smart sensors 307 may store the collected andcalculated data or analysis for delivery directly to the data server305. In some embodiments, the data collection element 309 also includesa communications circuit that receives the sensor signal from thesensors 307 and provides the raw sensor signal to the data collectionelement 309, which saves a condition indicator based on the raw sensorsignal in live data storage 311.

In some embodiments, the data terminal 303 stores the sensor signal asthe condition indicator in the live data storage 311, and in otherembodiments, the data terminal 303 processes the raw sensor signal togenerate a condition indicator based on, or according to, the raw sensorsignal before storing the condition indicator. The data terminal 303 mayactively query a sensor 307, may receive a signal from a sensor 307, ormay sample a signal from a sensor 307 to acquire a raw data signal orsensor signal. The data terminal 303 may acquire the data signal at aparticular time in a flight or in response to one or more operatingconditions meeting a predetermined set of criteria. Thus, each conditionindicator data set may be associated with an operating condition. Thedata terminal 303 may acquire a data signal, by sampling a continuous orlive signal, or querying a sensor, when the data terminal 303 detectsthat flight conditions or operating conditions meet one or morecriteria. For example, the data terminal 303 may be an FCC, and maydetermine that the engines are in a maximum takeoff power (MTOP) statebased on the throttle and collective settings, and may acquire a datasignal indicating, for example, vibrations of one or more components ofan engine, transmission, gear train, or the like, or for fuel flow,power generation, transmission torque, or the like. In another example,the data terminal 303 may determine that the rotorcraft is in a hover,in forward flight, or in another flight state, and may acquire the datasignal during the flight state. Condition indicator data sets may beformed from measurements or condition indicators determined in relationto similar operating conditions to provide consistent data. For example,a first condition indicator data set may include condition indicatorsfor a main rotor transmission gear during MTOP across multiple flights,while a second condition indicator data set may include conditionindicators for the same main rotor transmission gear during hover acrossmultiple flights.

The data terminal 303 may store the data signal, a sample of the datasignal, condition indicators, and other sensor data or relevantidentifying information in the live data storage 311. In someembodiments, the data signal or condition indicator may be tagged with adate, time, and operating condition indicator information when stored inthe live data storage 311 for later transmission to the data server 305.

The data server 305 aggregates data from one or more data terminals 303.The data server 305 collects data in one or more condition indicatordata sets for aggregation and trend analysis, and may provide a report,alerts or other information related to detected trends for individualcondition indicators data sets. The data server 305 stores conditionindicators, trend information, and the like, in extended data storage313. A data analysis element 315 reads condition indicator informationand perform trend analysis or detection on the condition indicators, andsaves trend information generated by the trend analysis into theextended data storage.

The data server 305 may be, for example, a server that is remote fromthe data terminal 303, or may be local to, or the same device as thedata terminal. 303. In some embodiments, the data terminal 303 and dataserver 305 are both disposed in a vehicle such as a rotorcraft, and mayboth be implemented in one or more FCCs. In other embodiments, the dataterminal 303 may be implemented in a device that is distinct from thedevice implementing the data server 305. For example, the data terminal303 may be implemented in a dedicated monitoring computer or deviceusing, for example, a purpose built processor, microcontroller, or thelike, or may be implemented in an ECCU or other control computer, whilethe data server 305 is implemented in, for example, an FCC. In otherembodiments, the data server 305 may be a diagnostic computer, remoteserver, or the like, that is separate from the vehicle on which the dataterminal 303 is disposed. The data terminal 303 may transfer data to thedata server 305 by responding to query from a maintenance computer,automatically transferring data to a remote data server 305 through awireless connection, a manual transfer or download by a user using, forexample, a non-transitory computer readable medium such as a universalserial bus (USB) stick or secure digital (SD) card, or the like.

The data terminal 303 collects a series of condition indicators for eachoperating condition that the data server 305 monitors. In someembodiments, the data server 305 receives raw sensor data or a datasignal from the data terminal 303 or from the sensors 307 forming thedata terminal 303, and performs analysis to isolate condition indicatorsfrom the data signal or raw sensor data. In other embodiments, the dataterminal 303 performs the analysis and sends a processed data signalsuch as a condition indicator, data signal sample, or the like to thedata server.

In some embodiments, the data signal may indicate more than onecondition indicator, and processing the data signal may includeisolating a condition indicator from the data signal. For example, thedata terminal 303 may acquire a data signal from a vibration sensoradjacent to a main rotor transmission. The data signal may be a sampleof the sensor data over a predetermined period of time, and may includedata from multiple vibration sources. The data signal may be analyzed,by the data terminal 303 or the data server 305, by, for example,filtering using Fourier analysis or the like, to isolate signals forindividual condition indicators. A Fourier transform may be applied to adata signal to transform the data signal into a filtered signal such asa frequency domain signal, which will indicate the amplitude ofdifferent subsignals making up the data signal. The subsignals atdifferent frequencies may be associated with different conditionindicators. For example, a first gear rotating at 300 RPM willcorrespond to a frequency signal at a first frequency in the filteredsignal, while a second gear rotating at 2000 RPM will correspond to afrequency subsignal at a second frequency in the filtered signal. Thus,a first vibration condition indicator for the first gear will correspondto a first vibration subsignal having a frequency of about 5 Hz, while asecond vibration condition indicator for the second gear will correspondto a second vibration subsignal having a frequency around 33.3 Hz. Thus,multiple condition indicators may be determined from a single datasignal or a single sensor. In some embodiments, the sensors 307 mayperform the processing of the data signals to determine the conditionindicators, and may send, for example, a data packet or other datasignal to data terminal 303. In other embodiments, the sensors 307 maysend the data signal to the data collection element 309 of the dataterminal 303, which processes the data signal to determine the conditionindicators. In another embodiment, the sensors 307 may send the datasignal to data terminal 303, which then passes the data signal to thedata server 305 for determination of the condition indicators.

The data server 305 attempts to detect trends of condition indicatorswhere the trend deviates from a normal or expected value. In someembodiments, the data server 305 has an alert system 317 that providesan alert indicating that a trend of a condition indicator has exceeded aparticular threshold such as a static indicator threshold, static trendthreshold or an adaptable trend threshold, a combination of the same, orthe like. The alert system 317 may provide the alert to a vehicleoperator, a maintenance technician, a fleet operator, a vehicle, owner,or to an automated system. The alert system 317 may be disposed in thevehicle, and include a cockpit indicator that is an audible indicatorsuch as a buzzer or voice prompt provided through a flight directorsystem, a graphic warning such as a note or other warning on a graphicscreen, instrument screen, flight director screen, or the like, or maybe provided as a dedicated visual indicator such as a dedicated awarning light, lamp, or the like. In other embodiments, the alert system317 may be remote from the vehicle, and may provide an automated alertby generating a report with a list of conditions of concern,automatically messaging a technician or owner, providing an indicator ona monitoring system, or the like. For example, a data server 305 may bea monitoring server at a fleet operator, and, as each vehicle returns tothe fleet base, the vehicle may automatically transmit conditionindicator data to the data server by way of a wireless link, or througha maintenance computer connected to the vehicle by a technician. Thedata server 305 may aggregate the newly received condition indicatordata for one or more monitored performance parameters with existingcondition indicator or trend data for the relevant performanceparameter, and analyze the data for trends. Upon detecting that a trendor condition data for a particular operating parameter has exceeded aparticular threshold such as a static trend threshold or an adaptabletrend threshold, the data server 305 may generate one or more alertsignals, which may include generating a problem report indicating that aparticular vehicle system, element or the like needs to be inspected,replaced, or otherwise addressed by a technician. The report may begenerated in response to determining that the trend indicates a problem,or in response to query for the report. In other embodiments, the alertsystem 317 may generate an alert signal that automatically messages afleet operator, vehicle owner, maintenance technician, or the like byemail, short messaging system (SMS) message, text message, automatedvoice call, or the like. In other embodiments, the alert system 317 maygenerate the alert signal to display an indicator on a management webpage, maintenance checklist, vehicle record, or the like. In someembodiments, the alert may indicate a severity of a problem, with thedata server 305 comparing the trend data to multiple differentthresholds. Thus, the alert may indicate the severity of a trend for aparticular vehicle element. In some embodiments, a vehicle-born alertand a remote alert may be used in combination. For example, amaintenance or inspection alert may be generated for a transmission gearwhen trend data indicates that vibration of the gear has exceeded athreshold such as a static trend threshold or an adaptable trendthreshold, indicating that the transmission gear should be inspected forpossible damage. The alert system 317 may generate a warning or problemmessage such as automated remote message and/or an in-cockpit warningwhen the trend data exceeds a higher threshold, indicating that furtheruse of the gear should be avoided. Thus, the alert system 317 may takedifferent alert actions based on the comparison of the trend data todifferent thresholds.

The data server 305 may use data from one or more data collectionpoints, such as sensors 307, data collection elements 309, dataterminals 303, or like, to determine a trend for a particular conditionindicator. In some embodiments, the data server 305 uses one or moreEMAs for trend detection, with each EMA essentially acting as a one polelow-pass filter. The data server 305 may further use a difference indifferent EMAs in an MACD system for trend detection. Two EMAs appliedto the data of interest can be used to determine the trend in thecondition indicator. In some embodiments, the MACD system may use afirst EMA or long term EMA with a long window (EMA_(long)) and secondEMA or short term EMA with a short window (EMA_(short)), with the MACDbeing the difference between the two, for example,EMA_(short)-EMA_(long). Positive MACD values indicate an upward trend inthe data and negative values indicate downwards trends. A static trendthreshold may be used to set an alert at a certain level, eitherpositive, negative, or both.

Applying the MACD to the condition indicators permits the HUMS 301 todetect trends in the data rather than the absolute values of the data. Astatic indicator threshold is an absolute, constant, or fixed thresholdthat is compared to the condition indicator values. Thresholds used inconjunction with the MACD are trend thresholds that are compared to thetrend data indicated by the MACD. In some embodiments, the trendthreshold may be a static trend threshold that is constant or fixed, andthat is compared to the MACD values. In other embodiments, the trendthreshold may be an adaptable trend threshold that varies based, forexample, on one or more values of the condition indicators, the MACDvalues, EMA values, or the like. The trend threshold permits detectionof a condition indicator problem earlier than a static indicatorthreshold. This is because the trend threshold operates on the changesin the condition indicator values, rather than the values of thecondition indicators themselves, as with a static indicator threshold.The trend threshold detects changes in the condition indicator wellbefore a static indicator threshold. While a static indicator thresholdmay be lowered to detect potential problems in a condition indicatorearlier, this may lead to false alarms for noisier signals. Thus, trenddetection using a trend threshold tends to provide earlier detection,with fewer false alarms, than static indicator thresholds.

The use of the MACD with an adaptable trend threshold for trenddetection in place of simple statistical indicators such as standarddeviation, kurtosis, or the like, may avoid trends being over identifiedin non-trending noisy data signals, or under identified in trending,non-noisy, trending data signals. This is because such simplestatistical indicators do not indicate a significant difference betweena non-trending noisy data signal and a non-noisy data signals. However,the noise in a signal may be quantified as a measure of volatility. Forexample, taking the absolute value of the difference between adjacentpoints and the summing those values over a moving window provides moredifferentiation between noise and trend than does standard deviation orother simple statistical indicators. Additionally, this approach permitsthe adaptable trend threshold to adapt, or change, as the volatility ina condition indicator data set or signal changes, or to adapt to thevolatility of different condition indicator data sets.

The volatility measure for a noisy signal will be greater than thevolatility measure for a non-noisy signal, and is particularly usefulwhen, for example, simple statistical indicators—such as standarddeviation or kurtosis—indicate no difference or a small differencebetween the noisy and non-noisy signals. Therefore, the measure ofvolatility may be used for setting trend thresholds to determine trends.In some embodiments, the volatility measure V at time t is:

V(t)=Σ_(n=t-p) ^(t) |X(n)−X(n−1)|  (1)

Where V(t) is the volatility measure for a particular window, X is thevalue of the data being analyzed, such as the condition indicator data,at time t and (p+1) is the size of the window for the volatilitymeasure. For example, if the window size is 150 samples, then p=149,since X(n−1) would take the calculation back to X(t−150). Additionally,in some embodiments, the volatility measure value V(t) can be divided bya scaling factor (S) to determine the adaptable trend threshold to beused for trend detection. Therefore, the adaptable trend threshold attime t is:

$\begin{matrix}{{{Threshold}(t)} = \frac{V(t)}{S}} & (2)\end{matrix}$

Where V(t) is as defined in Equation (1) and S is predetermined scalingfactor. Nominally, this scaling factor S may of the same order as thewindow size (p+1) for the volatility measure V(t). Using the scalingfactor S, where S has the same value as the window size (p+1), gives theaverage volatility over the window. Higher values of S would increasesensitivity, while lower values of S decrease sensitivity. In otherembodiments, the scaling factor S may differ from the window size, andmay be adjusted to tune the sensitivity of the trend detection.

In some embodiments, the scaling factor S may be a function of the valueof data being analyzed, or a function of a fixed value. This permits foradjustment in sensitivity based on the absolute value of the data. Thus,a signal that is noisy, but whose peak values are still well belowstatic indicator thresholds should not result in trend detection. Forexample, a adaptable trend threshold may be defined as:

$\begin{matrix}{{{Threshold}(t)} = \frac{V(t)}{S\left( {X(t)} \right)}} & (3)\end{matrix}$

A trend is indicated when the MACD value exceeds the adaptable trendthreshold. The trend is determined according to:

$\begin{matrix}{{I(t)} = \left\{ \begin{matrix}{0,} & {{{MACD}(t)} < {{Threshold}(t)}} \\{1,} & {{{MACD}(t)} \geq {{Threshold}(t)}}\end{matrix} \right.} & (4)\end{matrix}$

Where I(t) is the trend indicator function, with a 1 for I(t) indicatinga trend, an 0 for I(t) indicating no trend, and where MACD(t) is definedas:

$\begin{matrix}{\begin{matrix}{{{MACD}(t)} = \left( {{{EMA}_{A}(t)} - {{EMA}_{B}(t)}} \right)} \\{= \left\{ {\left( \frac{\left\lbrack {{\left( {k_{A} - 1} \right) \times {{EMA}_{A}\left( {t - 1} \right)}} + {X(t)}} \right\rbrack}{k_{A}} \right) -} \right.} \\\left. \left( \frac{\left\lbrack {{\left( {k_{B} - 1} \right) \times {{EMA}_{B}\left( {t - 1} \right)}} + {X(t)}} \right\rbrack}{k_{B}} \right) \right\}\end{matrix}\quad} & (5)\end{matrix}$

In some embodiments, the EMAs are defined based on two different kvalues k_(A), k_(B). In some embodiments, the k values may be related,such as k_(A)<k_(B) where k_(A)˜k_(B)/5). In some embodiments, k_(A)=30and k_(B)=150. Thus, the MACD uses a long term EMA (k_(B)=150) and ashort term EMA (k_(A)=30). Additionally, the EMA is a single-pole lowpass filter, however, other filters, including more complex filters,could also be used in other embodiments.

FIG. 4A is a graph 401 illustrating the calculation of EMA values 403and 405 and MACD values 409 according to some embodiments, and FIG. 4Bis a graph 413 illustrating condition indicator data 415 and a trendindication 417 according to some embodiments. The EMA values 403 and 405and MACD values 409 are generated using MACD analysis on the conditionindicator data 415. Short term EMA values 403 are calculated with windowvalue of 30, and long term EMA values 405 are calculated with windowvalue of 150. The difference, as calculated according Equation 5, above,gives the MACD values 409, and in some embodiments, may be a differencecalculation without additional filtering through another EMA.Additionally, in this example, a static trend threshold 407 of 1.2 isapplied. The trend indication 417 is a comparison of the MACD value 409to the static trend threshold 407, which may be performed according toEquation 4, above. The trend indication 417 shows that just a fewperiods of time would be flagged as trending.

FIG. 5A is a graph 501 illustrating a trend indication using the statictrend threshold 407 with noisy condition indicator data 503 according tosome embodiments. The condition indicator data 503 is noisier than thecondition indicator data 415 of FIG. 4B. The noisier condition indicatordata 503 has no discernable trend from visual inspection, compared tothe condition indicator data 415. Applying the same static trendthreshold 407 to the noisier condition indicator data 503 data yields asignificant number of trending indications in the trend data 505.

FIG. 5B is a chart 521 illustrating the calculation of EMA values 523and 525 and MACD values 529 in comparison to a static trend threshold527 according to some embodiments. The MACD values 529 are determinedfrom the noisy condition indicator data 503 (FIG. 5A), compared againstthe static trend threshold 527, are used to determine the trendindication in FIG. 5A, and show that the value of 1.2 is “in the noise”of the MACD values 529. In some embodiments, the static trend threshold527 may be, in this case, higher to account for this noise. In someembodiments, the data server 305 implements a different trend thresholdfor each condition indicator data set.

FIG. 5C is a chart 541 illustrating the calculation of EMA values 543and 545 and MACD values 549 in comparison to an adaptable trendthreshold 547 according to some embodiments. For comparison purposes,the EMA values 543 and 545 and MACD values 549 in FIG. 5C are the sameas the EMA values 523 and 525 and MACD values 529 in FIG. 5B. In someembodiments, the data server 305 implements a non-static trendthreshold, and that trend threshold may be determined according to thevolatility of the condition indicator data 503. The illustratedadaptable trend threshold 547 may be dynamically calculated according toa scaling factor, as shown in Equation 2, according to a scalingfunction and at least one previous condition indicator data value, asdescribed in Equation 3, or according to another function. In someembodiments, the adaptable trend threshold 547 is determined accordingto a scaling factor S, where S is determined according to the size of awindow for one of the EMAs 543 and 545. In some embodiments, the windowssize for the larger EMA is used as the scaling factor S in Equation 2.For example, the larger EMA may use a window size of 150, or covering150 data points. Thus, a scaling factor S of 150 may be used tonormalize or average the volatility according to Equation 2 todynamically determine the adaptable trend threshold 547. The adaptabletrend threshold 547, as dynamically calculated from the conditionindicator data 503 varies between 2 and 4 based on the changes in thevolatility of the signal itself. This is in contrast to the static trendthreshold 527 with a value of 1.2 in FIG. 5B. The values of theadaptable trend threshold 547 are higher than the MACD values 549,indicating that a trend does not exist.

FIG. 5D is a chart 561 illustrating the calculation of EMA values 563and 565 and MACD values 569 in comparison to an adaptable trendthreshold 567 according to some embodiments. For comparison purposes,the EMA values 563 and 565 and MACD values 569 are the same as the EMAvalues 403 and 405 and MACD values 409 of FIG. 4A, and FIG. 5A includesthe adaptable trend threshold 567 instead of the static trend threshold407 of 1.2 in FIG. 4A. The chart 561 illustrates the use of conditionindicator data that is less noisy than that used for FIG. 5C, andillustrates that the adaptable trend threshold 567 does not impair theability to detect a true trend. The same algorithm, namely the trendthreshold algorithm of Equation 2 with a scaling factor S of iso,results in a fairly low adaptable trend threshold 567. This adaptabletrend threshold 567 is lower than the adaptable trend threshold 547 ofFIG. 5C, which used noisier data. Additionally, the calculating theadaptable trend threshold 567 using the volatility is, in some cases,lower than the 1.2 static trend threshold 407 and 527 of FIGS. 4A and5B, respectively. Thus, the adaptable trend threshold provides aself-adjusting feature that permits the data terminal 303 to use thealgorithm for the adaptable trend threshold with multiple, anddifferent, condition indicator data sets without requiring thecalculation of a customized fixed threshold for each condition indicatordata set.

FIG. 6A is a graph 601 illustrating condition indicator data 603 and atrend indication 605 according to some embodiments, and FIG. 6B is agraph 621 illustrating the calculation of EMA values 623 and 625 andadaptable trend threshold 627 according to some embodiments. The highvalue of the trend indication 605 series means that a trend isindicated. The adaptable trend threshold 627 and MACD values 629 linesindicate a single good detection (around 2100) where the MACD values 629exceed the adaptable trend threshold 627. The trend indication 605further illustrates a correlation between a rise in the EMAs 623 and 625a rise in the condition indicator data 603. Thus, the adaptable trendthreshold 627 is relatively insensitive to the noise that precedes thetrend.

FIG. 7A is a graph 701 illustrating condition indicator data 703 and atrend indication 705 according to some embodiments, and FIG. 7B is agraph 721 illustrating the calculation of EMA values 723 and 725 andadaptable trend threshold 727 according to some embodiments. Thecondition indicator data 703 includes a single value spike that resultsin the indicated trend 707 corresponding to the spike. High spikes incondition indicator data values may lead to transient trend detectionswhen the spike is significantly greater than the remaining conditionindicator data. For example, the single spike in the condition indicatordata 703 causes a corresponding spike in the short term EMA 723 thatsignificantly exceeds the value of the long term EMA 725, even thoughthe long term EMA 725 also spikes. The significant difference in theEMAs 723 and 725 results in a significant spike in the MACD value 729,which exceeds the adaptable trend threshold 727 and results in a trendbeing indicated.

An improved low pass filter with, for example, more poles, may improvethe ability to reject these spikes. In some embodiments, Butterworth orChebyshev filters with different cut-off frequencies (to simulate thetwo windows in the EMA filters) may be used instead the EMAs. Thedifferent cut-off frequencies for the various filters may correspond tothe different EMA windows. For example, the large term EMA 723 maycorrespond to a first cut-off frequency, and the short term EMA 723 maycorrespond to a second cut-off frequency that is higher than the firstcut-off frequency. Additionally, other filters such as Kalman filters,Hodrick-Prescott, and the like, may alternatively be used singly, or incombination, to replace one or more the EMAs. For example, for theHodrick-Prescott filter, the different cut-off frequencies correspond todifferent values of the multiplier lambda (λ), with a larger lambda usedfor a larger window EMA and a smaller lambda used for a smaller windowEMA.

Lagging the MACD value by 1 (or more) samples versus the volatility oradaptable trend threshold permits the trend threshold to “learn” fromthat data while minimally delaying the indication of the trend. Thus,the MACD may be calculated using a calculation window that lags thecalculation window used for calculating the trend threshold or thevolatility by one or more samples or condition indicator values. Thislag between the MACD calculation and the volatility or adaptable trendthreshold avoids sensitivity to data spikes since the adaptable trendthreshold includes the spike data before the MACD calculation includesthe spike data. The volatility and trend threshold may be calculatedfrom a condition indicator data set that includes the current conditionindicator, while the MACD is calculated using a portion of the data setwhere each condition indicator is prior to the current conditionindicator. This may be performed, in some embodiments, by usingdifferent or offset calculation windows for the volatility data used tocalculate the adaptable trend threshold and the MACD. For example, theMACD may be offset from the volatility calculation by one data point,using data from condition indicators preceding the current conditionindicator:

$\begin{matrix}{\begin{matrix}{{{MACD}(t)} = \left( {{{EMA}_{A}(t)} - {{EMA}_{B}(t)}} \right)} \\{= \left\{ {\left( \frac{\left\lbrack {{\left( {k_{A} - 1} \right) \times {{EMA}_{A}\left( {t - 2} \right)}} + {X\left( {t - 1} \right)}} \right\rbrack}{k_{A}} \right) -} \right.} \\\left. \left( \frac{\left\lbrack {{\left( {k_{B} - 1} \right) \times {{EMA}_{B}\left( {t - 2} \right)}} + {X\left( {t - 1} \right)}} \right\rbrack}{k_{B}} \right) \right\}\end{matrix}\quad} & (6)\end{matrix}$

In Equation 6, the MACD is calculated using EMA_(A)(t−1) andEMA_(B)(t−1), shifting the index to point the EMA calculation to aslightly different set of data by removing the most recent data pointand adding an older data point. Offsetting the MACD calculation from thetrend threshold calculation buffers the MACD calculation and smooths outthe trend detection.

FIG. 8A is a graph 801 illustrating condition indicator data 803 and atrend indication 805 according to some embodiments. FIG. 8B is a graph821 illustrating the calculation of EMA values 823 and 825 and adaptabletrend threshold 827 according to some embodiments. In FIG. 8B, the MACDvalue 829 is determined in a period that is offset from determination ofthe adaptable trend threshold 827. In FIG. 8A, the condition indicatordata 803 has spikes around 350, 750, and 800, which almost cause a trendindication, but the offset or lag of 1 data point between calculatingthe trend threshold and the MACD avoided the trend being indicated.However, the trend indication 805 indicates a trend around 1400, wherethe condition indicator data has a prolonged spike.

In some embodiments, having some hysteresis for turning the trendindication on or off prevents prevent dithering, where the trendindication rapidly switches between on and off. For example, having thetrend indication come “on” at the calculated threshold level, but havingthe trend indication go “off” at a predetermined level associated withthe adaptable trend threshold permits the data server 305 to smooth thetrend indication. Thus, in an embodiment, a trend may be indicated whenthe MACD values are equal to, or exceed, the trend threshold, and mayremain on until the MACD falls below, for example, 90% of the calculatedtrend threshold value. Therefore, the data server may determine that thetrend is not indicated in response to the MACD value being less than apredetermined level of the trend threshold, may determine that the trendis indicated in response to the trend being indicated for a precedingcondition indicator and the MACD value being equal to, or greater than,the predetermined level of the trend threshold.

In other embodiments, different levels of trend indication may be basedon preset levels related to the adaptable trend threshold. For example,an alert system in the data server 305 may indicate a green light or OKstate for a condition indicator when the MACD is below a first thresholdlevel, such as 80% of the adaptable trend threshold. The alert systemmay indicate a yellow light or warning indicator when the MACD isbetween the first threshold level and a second, higher, threshold level,such as 100% of the adaptable trend threshold, and may indicate a redlight or critical alert when the MACD is above the second thresholdlevel.

FIG. 8C is a graph 841 illustrating a variety of scaling functionaccording to some embodiments. In some embodiments, the adaptable trendthreshold may be determined using a function for S in, for example,Equation 3. In some embodiments, scaling function S is a step function843, a linear function 847, or a hybrid or combination of step andlinear functions 845.

The sensitivity of the trend threshold calculation can itself be afunction of the absolute value of a condition indicator data element asshown in Equation 3. In some embodiments, when the condition indicatordata is close to a static indicator threshold, the data terminal is moresensitive to detecting a trend, but when the condition indicator data isfar from the static indicator threshold, the data terminal is lesssensitive to detecting a trend. Thus, trends which appear to berelatively significant, relative to the signal, but that are smallrelative to range of interest, or close to the static indicatorthreshold, could be ignored. In the simplest form, a minimum thresholdthat is a percentage of the static indicator threshold could be set.

In some embodiments, scaling function S(X(t)) is a step function 843dependent on X(t), and may be used to adjust the sensitivity of thetrend threshold as shown in Equation 3, so that the trend threshold isadjusted as X(t) increases. In some embodiments, the scaling functionS(X(t)) could be a step function:

$\begin{matrix}\left( {{S\left( {X(t)} \right)} = \left\{ \begin{matrix}{Y,} & {{X(t)} < A} \\{Z,} & {{X(t)} \geq A}\end{matrix} \right.} \right. & (7)\end{matrix}$

Where Z>Y. In one embodiment, threshold A is based on the staticindicator threshold for the data being analyzed, and in some embodimentS, may be, for example, 20% of the static indicator threshold. For thesimple step function 843, as shown in FIG. 8c , a static indicatorthreshold of 10 may be of interest, and threshold A may be set to 2based on the static indicator threshold. In such an example, anindicator value of 2 or lower may be of little concern, regardless ofany trend indication. Therefore, the trend-based alert may take thatinto account by ignoring, or specially handling, trend alerts when thevalue of the condition indicator is below a lower static indicatorthreshold, or cut-off value.

In another embodiment, the scaling function S(X(t)) is a linear function847:

(S(X(t))=m*X(t)+b  (8)

Where m is a slope and b is an intercept. For the illustrated linearfunction 847, the intercept b=0 and slope m=15.

In another embodiment, the scaling function S(X(t)) could be acombination step and linear function 845, where values below a firsttrend threshold A have minimal, fixed, sensitivity, values between A anda second trend threshold B have increasing sensitivity according to alinear function, and values above B have a fixed sensitivity. For thestep/linear combination function 845, the value of first trend thresholdA is 2, the slope m=25, and the value the second trend threshold B=7.The combination step and linear function 845 has a fixed sensitivitybelow 2, a ramp up in sensitivity to 7, and then that fixed sensitivityabove 7.

While only one linear function is illustrated shown, it should beunderstood that multiple linear functions may be combined into differentzones. Additionally, the scaling function S(X(t)) is not limited tolinear or step functions, but may be polynomial, parabolic, exponential,logarithmic, hyperbolic functions, or another nonlinear function, acombination of non-linear functions, or a combination of non-linearfunctions, linear and step functions.

Additionally, trend detection using the MACD may be combined withdetection of trends using static indicator thresholds, or monitoringother long term trends. The MACD trend detection is well suited fordetecting the large changes in the MACD, catching issues that aretrending up fairly quickly. However, the MACD trend detection approachtends to under-identify trends that are rising relatively slowly. Theslowly rising trends are more easily detectable using absolute trends,or static indicator thresholds. Therefore, in some embodiments, the MACDtrend detection using a trend threshold may be combined with anothertrend detection process, such as comparison of indicator data to astatic indicator threshold, comparison of the long term EMA or other EMAto a static indicator threshold, comparison of the rate of change of anEMA such as a long term EMA of the condition indicator data to athreshold. Therefore, a trend indication may be indicted when the MACDexceeds a static or adaptable trend threshold, or when an EMA indicatesa long term trend exceeds another threshold such as a static indicatorthreshold or rate of change threshold. Similarly, the MACD trenddetection approach may be combined with one or more statistical measuresso that the process finds spikes or changes in statistical scatter inaddition to identifying trends in the condition indicator data.

FIG. 9A is a flow diagram illustrating a method 901 for providing analert signal for condition indicator trends according to someembodiments. The method may be performed by a system having a dataterminal and data server, and may be embodied in a software programstored on a non-transitory computer-readable storage medium that isexecuted by a processor of the data terminal and a processor of the dataserver.

In block 903, one or more sensor signals are received. In someembodiments, a sensor generates the sensor signal and provides thesensor signal to a data collection element in a data terminal. Thesensor may continuously send the sensor signal for sampling by the datacollection element, may send the sensor signal in response to a query,activation signal or the like, may send the sensor signal in response toa predetermined condition, may store the sensor signal for processing atthe sensor device, or may send or provide the signal according toanother procedure. A condition indicator is generated in block 905. Insome embodiments, one or more condition indicators are generated from asensor signal, with different condition indicators being derived fromdifferent subsignals in the sensor signal. In other embodiments,multiple readings from a sensor, or readings from multiple sensors maybe combined or otherwise used to generate the condition indicator. Forexample, data signals form an accelerometer and from a tachometer may beused to generate synchronous time average vibration data or conditionindicators. Additionally, the condition indicator may be generated atthe sensor, at a data terminal or at a data server.

In block 907, a first moving average is determined, and in block 909 asecond moving average is determined. In some embodiments, the first andsecond moving averages may each be EMAs, and in other embodiments, oneor both of the moving averages are other types of filters. Additionally,the moving averages may be determined according to the conditionindicator and a previously determined moving average or moving averagecovering a different portion of the condition indicator data set. Insome embodiments, the moving averages have different window sizes, andthe first moving average may have a first window size that is smallerthan a window size of the second moving average. For example, the firstmoving average may have a window size that is about one fifth of thesize of the window size for the second moving average, and is in someembodiments, 30 data points, while the window size for the second movingaverage is about 150 data points. In block 911, the difference in themoving averages is determined. In some embodiments, the difference,combined with differences in previously determined moving averages formsa series of MACD values.

In block 913, the volatility for the condition indicator is determined.In some embodiments, the volatility is a sum of changes in the conditionindicators over a selected window. In block 915, a trend threshold forthe condition indicator is determined, and may, in some embodiments, bean adaptable trend threshold. In some embodiments, the trend thresholdmay be an adaptable trend threshold that is offset from the last dataused to determine the moving averages and moving average difference. Insome embodiments, the threshold may be an adaptable trend thresholdbased on the volatility, may be adjusted using a scaling factor S or ascaling function S(X(t)), and may be determined by dividing thevolatility by the scaling factor or scaling function. In someembodiments, the scaling function is a linear function, a steppedscaling function of a combination of linear and stepped functions.

In block 917, the difference in the moving averages is compared to thetrend threshold, and in block 919 a trend indication is determined. Insome embodiments, the trend is indicated based on the comparison of themoving average difference to the trend threshold, and in someembodiments, the trend indication uses hysteresis, where a trend isindicated when the trend initially exceeds the trend threshold, and thetrend indication is maintained until the moving average difference fallsbelow a hysteresis trend threshold that is lower than the trendthreshold and is based on the trend threshold. In some embodiments, themoving average and trend data are provided in block 921. In someembodiments, the moving average and trend data may be provided through adata server as part of a chart, report, display or the like.

The collection of sensor data, determining of the threshold, movingaverage and moving average difference, comparison of the of thedifferent to the threshold and determining the trend indication may berepeated any number of times and performed periodically or continuously.Thus, for example, after determining a trend indication in block 919,the system may receive a new sensor signal in block 903 and repeat themethod 901, or, in another example, after determining the threshold inblock 915, the system may generate a new condition indicator in block905 from a newly received sensor signal and repeat the method 901.

In block 923, a determination is made on whether a trend is indicated,and if the trend is indicated, then an alert signal is provided in block925. In some embodiments, the alert system or data server may activatean in-vehicle alert, for example, by lighting a trouble lamp in avehicle cockpit, displaying a warning message on a cockpit display, orby providing an out-of-vehicle message by automatically message a fleetoperator, vehicle owner, maintenance technician, or the like by email,short messaging system (SMS) message, text message, automated voicecall, or the like. In some embodiments, the alert system may generate analert and display an indicator on a management web page, maintenancechecklist, vehicle record, or the like. In some embodiments, the alertsignal is a problem report generated by a data server indicating aproblem or warning for the system or element related to the conditionindicators. Additionally, the alert system may indicate a level ofproblem or alert, for example, by providing an alert severity. In someembodiments, an in-vehicle alert and a remote alert may be used incombination.

FIG. 9B is a flow diagram illustrating a method 951 for providing analert signal for quickly changing and slowly changing conditionindicator trends according to some embodiments. In such an embodiment, asystem may use, for example, MACD analysis to determine whether acondition indicator indicated a quickly changing trend, and alsodetermine whether a long term, or slowly changing, trend is indicated.

In the method 951 for providing an alert signal for quickly changing andslowing changing condition indicator trends, data collection anddetermination of the MACD is performed similarly to the method of FIG.9A. In block 953, one or more sensor signals are received. A conditionindicator is generated in block 955.

In block 957, a first moving average is determined, and in block 959 asecond moving average is determined. In some embodiments, the first andsecond moving averages may each be EMAs, as discussed above.Additionally, the EMAs may include a short term EMA and a long term EMA.In other embodiments, one or both of the moving averages are other typesof filters, with at least one of the moving averages being a long termmoving average. In block 961, the difference in the moving averages isdetermined. In some embodiments, the difference, combined withdifferences in previously determined moving averages forms a series ofMACD values. The MACD values may be used to determine whether a quicklychanging trend is indicated by the condition indicator data.

In block 963, the volatility for the condition indicator is determined,and in block 965, a trend threshold for the condition indicator isdetermined. In some embodiments, the trend threshold is an adaptabletrend threshold that may be based on the volatility. In block 967, thedifference in the moving averages is compared to the trend threshold,and in block 969 a quickly changing trend indication is determined. Insome embodiments, the quickly changing trend is indicated based on thecomparison of the moving average difference or MACD to the trendthreshold, and may be the same as described above with respect to FIG.9A. In some embodiments, the moving average and trend data are providedin block 971 for display or later use.

In block 973, a determination is made on whether a quickly changingtrend is indicated, and if the trend is indicated, then an alert signalis provided in block 975, and may be an alert similar to that of FIG.9A. If, in block 973, a quickly changing trend is not indicated, thesystem may attempt to determine whether a slowly changing trend isindicated. The MACD analysis method may be used to catch quicklychanging trends in the condition indicators, and a long term, data from,for example, a long term EMA or other long term moving average may beused to determine whether a long term or slowly changing trend isindicated.

In block 977, a Rate of change of a moving average is determined. Insome embodiments, a derivative or other analysis is performed on, forexample, the long term EMA that was calculated as part of the MACDcalculation.

While the rate of change of the long term average may be used todetermine slowly changing trends, the rate of change in trend conditionindicator data tends to inaccurately detect quickly changing trends, asthe rate of change tends to amplify noise in the existing signal, andresult in many false alarms. Using the rate of change in combinationwith MACD analysis permits detection of slowly changing trends andquickly changing trend. The MACD analysis removes indicators of quicklychanging trends from consideration, avoiding false alarms that wouldmake use of the rate of change approach will be impractical. Performingthe MACD analysis prior to the rate of change analysis identifiesindicators with quickly changing trends, removing them fromconsideration before applying rate of change analysis that is limited tothe long-term EMA of the remaining indicators to find slowly changingtrends.

In block 981, the rate of change is compared to a rate of changethreshold according to, for example:

ROC(EMA(150))>Q  (9)

For example, a rate of change threshold Q may be a static or adaptivethreshold, and may be compared to the rate of change ROC of a long termEMA (EMA(150) having, for example, a window size of 150 samples. In someembodiments, the rate of change threshold Q may be based on the value ofthe condition indicator.

In block 983, a slowly changing trend indication is determined. In someembodiments, the slowly changing trend indication may be based on thecurrent value of the rate of change for the long term average or EMAexceeding the rate of change threshold Q. In other embodiments, the rateof change may be used to predict whether a trend will exceed a rate ofchange threshold or other threshold, within a predetermined time. Thus,the system may assume that a long term rate of change will continue atsubstantially the same rate of change, and project the time that wouldbe needed for the long term average to exceed the threshold. In such anembodiment, a notification window may be used to determine whether aslowly changing trend is indicated by comparing the projected timeneeded to exceed the threshold falls within, or is less than, thenotification window. For example, if, based on the rate of change andthe current value of the indicator, a long term average may be projectedto reach a static indicator threshold in 2 years. If the projected timeto reach the static indicator threshold exceeds the notification window,the system may not provide an alert. However, if the current value ofthe indicator is higher (for the same rate of change) and the long termaverage would reach the static indicator threshold in projected time of3 weeks, the projected time may be within, or less than, thenotification window, and a slowly changing trend may be indicated.

In some embodiments, a first type of alert may be provided for a currentslowly changing trend indication, and a different type of alert may beprovided for a projected slowly changing trend indication. For example,a critical alert may be provided if the rate of change of a long termEMA exceeds a rate of change threshold, and a warning alert may beprovided if the rate of change is below the rate of change threshold,but the value of the long term EMA is projected to exceed the staticindicator threshold within a time period less than an indication window.

In block 985, a determination is made on whether the slowly changingtrend is indicated, and if the slowly changing trend is indicated, thenan alert signal is provided in block 975, and may be an alert similar tothat discussed previously.

In block 979, one or more statistical analysis processes may be appliedto determine whether a data spike has occurred in the relevant data. Insome embodiments, the statistical analysis may be applied to thecondition indicator data, to the MACD, to the rate of change of the longterm EMA, or another data set or data point. In an embodiment, thesystem may, for example, compare the indicator value to a statisticalthreshold based on statistics of a moving window of the indicator suchas standard deviation. For example, if a condition indicator value isabove the statistical threshold, such as four (4) standard deviations,and a quickly changing trend is not indicated, then the indicator valuemay be identified as a data spike. Thus, identifying real, quicklychanging trends before analysis of data using long term trend analysisor statistical spike analysis reduces the likelihood of missing quicklychanging trends or falsely identifying data spikes as quickly changingtrends. In some embodiments, a spike alert may be provided in block 975if a spike is detected. The spike alert may be displayed to a user toalert the user to a potential catastrophic problem, such as a partfailure, or to alert the user that the data spike may have caused a longterm trend indication. Additionally, in some embodiments, if thestatistical analysis indicates that a data spike has occurred in a dataseries, the system may ignore an associated long term trend indication,the data point may be discarded, further calculation of a long termmoving average may be avoided, or another action may be taken.

FIG. 10 is a diagram illustrating a computer system 1001 that may beused to implement a system, data terminal, or data server according tosome embodiments. The computer system 1001 can include an input/output(I/O) interface 1003, an analysis engine 1005, and a database 1007.Alternative embodiments can combine or distribute the I/O interface1003, the analysis engine 1005, and the database 1007, as desired.Embodiments of the computer system 1001 may include one or morecomputers that include one or more processors and memories configuredfor performing tasks described herein. This can include, for example, acomputer having a central processing unit (CPU) and non-volatile memorythat stores software instructions for instructing the CPU to perform atleast some of the tasks described herein. This can also include, forexample, two or more computers that are in communication via a computernetwork, where one or more of the computers include a CPU andnon-volatile memory, and one or more of the computer's non-volatilememory stores software instructions for instructing any of the CPU(s) toperform any of the tasks described herein. Thus, while the exemplaryembodiment is described in terms of a discrete machine, it should beappreciated that this description is non-limiting, and that the presentdescription applies equally to numerous other arrangements involving oneor more machines performing tasks distributed in any way among the oneor more machines. It should also be appreciated that such machines neednot be dedicated to performing tasks described herein, but instead canbe multi-purpose machines, for example computer workstations, that aresuitable for also performing other tasks.

The I/O interface 1003 can provide a communication link between externalusers, systems, and data sources and components of the computer system1001. The I/O interface 1003 can be configured for allowing one or moreusers to input information to the computer system 1001 via any knowninput device. Examples can include a keyboard, mouse, touch screen,and/or any other desired input device. The I/O interface 1003 can beconfigured for allowing one or more users to receive information outputfrom the computer system 1001 via any known output device. Examples caninclude a display monitor, a printer, cockpit display, and/or any otherdesired output device. The I/O interface 1003 can be configured forallowing other systems to communicate with the computer system 1001. Forexample, the I/O interface 1003 can allow one or more remote computer(s)to access information, input information, and/or remotely instruct thecomputer system 1001 to perform one or more of the tasks describedherein. The I/O interface 1003 can be configured for allowingcommunication with one or more remote data sources. For example, the I/Ointerface 1003 can allow one or more remote data source(s) to accessinformation, input information, and/or remotely instruct the computersystem 1001 to perform one or more of the tasks described herein.

The database 1007 provides persistent data storage for the computersystem 1001. Although the term “database” is primarily used, a memory orother suitable data storage arrangement may provide the functionality ofthe database 1007. In alternative embodiments, the database 1007 can beintegral to or separate from the computer system 1001 and can operate onone or more computers. The database 1007 preferably providesnon-volatile data storage for any information suitable to support theoperation of the flight control system 201 and the method 500, includingvarious types of data discussed further herein. The analysis engine 1005can include various combinations of one or more processors, memories,and software components.

An embodiment system includes a sensor operable to measure an operatingcondition of a vehicle and generate a sensor signal associated with theoperating condition and a data server operable to acquire a currentcondition indicator of a condition indicator set according to the sensorsignal, and to determine whether a trend in the condition indicator setis indicated according to at least the current condition indicator, atleast one previous condition indicator of the condition indicator setand a volatility of at least a portion of the condition indicator set.The data server is further operable to provide an alert in response todetermining that the trend is indicated.

In some embodiments, the system further includes a data terminal. Thedata terminal is operable to acquire the sensor signal from the sensor,and to store the sensor signal and send the stored sensor signal to thedata server. In some embodiments, the data server is further operable togenerate the current condition indicator from the sensor signal afterreceiving the sensor signal from the data terminal. In some embodiments,the operating condition of the vehicle is a vibration of the vehicle,and the current condition indicator is a vibration associated with anoperating element of the vehicle determined according to a subsignal ofthe sensor signal or a feature of the sensor signal. In someembodiments, the data server is operable to determine whether the trendindicated according to the volatility of at least the portion of thecondition indicator set and further according to a moving averageconvergence divergence (MACD) value determined from a first exponentialmoving average (EMA) of the condition indicator set, and a second (EMA)of the condition indicator set, and the first EMA has a first windowsize and the second EMA has a second window size smaller than the firstwindow size. In some embodiments, the data server is operable todetermine an adaptable trend threshold according to the volatility of atleast a first portion of the condition indicator set, where the firstportion of the condition indicator set includes the current conditionindicator, and where the data server is further operable to determinethat the trend in the condition indicator set is indicated in responseto the MACD value being equal to, or greater than, the adaptable trendthreshold. In some embodiments, the data server is further operable todetermine the adaptable trend threshold according to the volatility ofat least a first portion of the condition indicator set divided by oneof a scaling factor or a scaling function. The scaling factor is equalto the first window size, the scaling function is one of a stepfunction, a linear function or a combination of linear and stepfunctions, and the scaling function is a function of the currentcondition indicator.

An embodiment data server includes a processor and a non-transitorycomputer-readable storage medium storing a program to be executed by theprocessor. The program including instructions for acquiring a currentcondition indicator of a condition indicator set associated with anoperating condition of a vehicle, with the condition indicator setindicating sensor readings associated with an operating element of thevehicle under the operating condition, and determining a volatility of afirst portion of the condition indicator set, where the first portion ofthe condition indicator set includes the current condition indicator.The program further includes instructions for determining one or moremoving averages of a second portion of the condition indicator set,determining whether a trend associated with the operating element isindicated according to the one or more moving averages and thevolatility, and generating an alert signal in response to thedetermining that the trend is indicated.

In some embodiments, the program further includes instructions forreceiving a sensor signal having one of the sensor readings, separatinga subsignal from the sensor signal, and determining the currentcondition indicator from the subsignal. In some embodiments, theinstructions for the determining the one or more moving averages includeinstructions for determining a first exponential moving average (EMA)for a first window of the second portion of the condition indicator set,and determining a second EMA for a second window of the second portionof the condition indicator set, wherein the second window has a sizesmaller than a size of the first window. In some embodiments, theinstructions for determining whether the trend is indicated includeinstructions for generating an MACD value according to the first EMA andthe second EMA, and determining whether the trend is indicated accordingto the MACD value and the volatility. In some embodiments, the programfurther includes instructions for determining an adaptable trendthreshold according to the volatility, and the instructions fordetermining whether the trend is indicated include instructions fordetermining that the trend is indicated in response to the MACD valuebeing equal to, or exceeding, the adaptable trend threshold. In someembodiments, the instructions for determining the adaptable trendthreshold include instructions for determining the adaptable trendthreshold to be the volatility divided by a scaling factor associatedwith the size of the first window. In some embodiments, the instructionsfor determining whether the trend is indicated according to the MACDvalue and the volatility include instructions for determining whether aquickly changing trend is indicated, and the program further includesinstructions for determining whether a slowly changing trend isindicated according to the first EMA and in response to determining thatthe quickly changing trend is not indicated, where the instructions forgenerating the alert signal include instructions for generating thealert signal in response to determining that the quickly changing trendis indicated or determining that the slowly changing trend is indicated.In some embodiments, the instructions for determining whether the trendis indicated include instructions for determining that the trend is notindicated in response to the MACD value being less than a predeterminedlevel of the adaptable trend threshold, wherein the predetermined levelof the adaptable trend threshold is less than a value of the adaptabletrend threshold, and determining that the trend is indicated in responseto the trend being indicated for a preceding condition indicator and theMACD value being equal to, or greater than, the predetermined level ofthe adaptable trend threshold. In some embodiments, each conditionindicator in the second portion of the condition indicator set is priorto the current condition indicator, and one or more calculation windowsfor the one or more moving averages lag a calculation window for thevolatility by at least one condition indicator.

An embodiment method includes acquiring a current condition indicator ofa condition indicator set associated with an operating condition of avehicle, with the condition indicator set indicating sensor readingsassociated with an operating element of the vehicle under the operatingcondition, determining, by a data server, a volatility of a firstportion of the condition indicator set, where the first portion of thecondition indicator set includes the current condition indicator,determining, by the data server, one or more moving averages of a secondportion of the condition indicator set, determining, by the data server,whether a trend associated with the operating element is indicatedaccording to the one or more moving averages and the volatility, andgenerating, by the data server, an alert signal in response to thedetermining that the trend is indicated.

In some embodiments, the determining the one or more moving averagesincludes determining a first exponential moving average (EMA) for afirst window of the second portion of the condition indicator set, anddetermining a second EMA for a second window of the second portion ofthe condition indicator set, where the second window has a size smallerthan a size of the first window, and the determining whether the trendis indicated includes generating an MACD value according to the firstEMA and the second EMA, determining an adaptable trend thresholdaccording to the volatility, and determining whether the trend isindicated in response to the MACD value being equal to, or exceeding,the adaptable trend threshold. In some embodiments, the adaptable trendthreshold is the volatility divided by one of a scaling factorassociated with the size of the first window or a result of a functionapplied to the current condition indicator, and the function is one of astep function, a linear function or a combination of linear and stepfunctions. In some embodiments, the determining whether the trend isindicated further includes determining that the trend is not indicatedin response to the MACD value being less than a predetermined level ofthe adaptable trend threshold, where the predetermined level of theadaptable trend threshold is less than a value of the adaptable trendthreshold, and determining that the trend is indicated in response tothe trend being indicated for a preceding condition indicator and theMACD value being equal to, or greater than, the predetermined level ofthe adaptable trend threshold. In some embodiments, each conditionindicator in the second portion of the condition indicator set is priorto the current condition indicator, and one or more calculation windowsfor the one or more moving averages each lag a calculation window forthe volatility by at least one condition indicator.

While this invention has been described with reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments, as well as other embodiments of the invention,will be apparent to persons skilled in the art upon reference to thedescription. It is therefore intended that the appended claims encompassany such modifications or embodiments.

What is claimed is:
 1. A system, comprising: a sensor operable tomeasure an operating condition of a vehicle and generate a sensor signalassociated with the operating condition; and a data server operable toacquire a current condition indicator of a condition indicator setaccording to the sensor signal, and to determine whether a trend in thecondition indicator set is indicated according to at least the currentcondition indicator, at least one previous condition indicator of thecondition indicator set and a volatility of at least a portion of thecondition indicator set, wherein the data server is further operable toprovide an alert in response to determining that the trend is indicated.2. The system of claim 1, further comprising a data terminal, whereinthe data terminal is operable to acquire the sensor signal from thesensor, and to store the sensor signal and send the stored sensor signalto the data server.
 3. The system of claim 2, wherein the data server isfurther operable to generate the current condition indicator from thesensor signal after receiving the sensor signal from the data terminal.4. The system of claim 1, wherein the operating condition of the vehicleis a vibration of the vehicle, and wherein the current conditionindicator is a vibration associated with an operating element of thevehicle determined according to a subsignal of the sensor signal or afeature of the sensor signal.
 5. The system of claim 1, wherein the dataserver is operable to determine whether the trend indicated according tothe volatility of at least the portion of the condition indicator setand further according to a moving average convergence divergence (MACD)value determined from a first exponential moving average (EMA) of thecondition indicator set, and a second (EMA) of the condition indicatorset, wherein the first EMA has a first window size and the second EMAhas a second window size smaller than the first window size.
 6. Thesystem of claim 5, wherein the data server is operable to determine anadaptable trend threshold according to the volatility of at least afirst portion of the condition indicator set, wherein the first portionof the condition indicator set includes the current condition indicator,and wherein the data server is further operable to determine that thetrend in the condition indicator set is indicated in response to theMACD value being equal to, or greater than, the adaptable trendthreshold.
 7. The system of claim 6, wherein the data server is furtheroperable to determine the adaptable trend threshold according to thevolatility of at least a first portion of the condition indicator setdivided by one of a scaling factor or a scaling function, wherein thescaling factor is equal to the first window size, wherein the scalingfunction is one of a step function, a linear function or a combinationof linear and step functions, and wherein the scaling function is afunction of the current condition indicator.
 8. A data server,comprising: a processor; and a non-transitory computer-readable storagemedium storing a program to be executed by the processor, the programincluding instructions for: acquiring a current condition indicator of acondition indicator set associated with an operating condition of avehicle, the condition indicator set indicating sensor readingsassociated with an operating element of the vehicle under the operatingcondition; determining a volatility of a first portion of the conditionindicator set, wherein the first portion of the condition indicator setincludes the current condition indicator; determining one or more movingaverages of a second portion of the condition indicator set; determiningwhether a trend associated with the operating element is indicatedaccording to the one or more moving averages and the volatility; andgenerating an alert signal in response to the determining that the trendis indicated.
 9. The data server of claim 8, wherein the program furtherincludes instructions for: receiving a sensor signal having one of thesensor readings; separating a subsignal from the sensor signal; anddetermining the current condition indicator from the subsignal.
 10. Thedata server of claim 8, wherein the instructions for the determining theone or more moving averages include instructions for: determining afirst exponential moving average (EMA) for a first window of the secondportion of the condition indicator set; and determining a second EMA fora second window of the second portion of the condition indicator set,wherein the second window has a size smaller than a size of the firstwindow; and wherein the instructions for determining whether the trendis indicated include instructions for: generating an MACD valueaccording to the first EMA and the second EMA; and determining whetherthe trend is indicated according to the MACD value and the volatility.11. The data server of claim 10, wherein the program further includesinstructions for determining an adaptable trend threshold according tothe volatility; and wherein the instructions for determining whether thetrend is indicated include instructions for determining that the trendis indicated in response to the MACD value being equal to, or exceeding,the adaptable trend threshold.
 12. The data server of claim 11, whereinthe instructions for determining the adaptable trend threshold includeinstructions for determining the adaptable trend threshold to be thevolatility divided by a scaling factor associated with the size of thefirst window.
 13. The data server of claim 10, wherein the instructionsfor determining whether the trend is indicated according to the MACDvalue and the volatility include instructions for determining whether aquickly changing trend is indicated; wherein the program furtherincludes instructions for determining whether a slowly changing trend isindicated according to the first EMA and in response to determining thatthe quickly changing trend is not indicated; and wherein theinstructions for generating the alert signal include instructions forgenerating the alert signal in response to determining that the quicklychanging trend is indicated or determining that the slowly changingtrend is indicated.
 14. The data server of claim 11, wherein theinstructions for determining whether the trend is indicated includeinstructions for: determining that the trend is not indicated inresponse to the MACD value being less than a predetermined level of theadaptable trend threshold, wherein the predetermined level of theadaptable trend threshold is less than a value of the adaptable trendthreshold; and determining that the trend is indicated in response tothe trend being indicated for a preceding condition indicator and theMACD value being equal to, or greater than, the predetermined level ofthe adaptable trend threshold.
 15. The data server of claim 8, whereineach condition indicator in the second portion of the conditionindicator set is prior to the current condition indicator; and whereinone or more calculation windows for the one or more moving averages laga calculation window for the volatility by at least one conditionindicator.
 16. A method, comprising: acquiring a current conditionindicator of a condition indicator set associated with an operatingcondition of a vehicle, the condition indicator set indicating sensorreadings associated with an operating element of the vehicle under theoperating condition; determining, by a data server, a volatility of afirst portion of the condition indicator set, wherein the first portionof the condition indicator set includes the current condition indicator;determining, by the data server, one or more moving averages of a secondportion of the condition indicator set; determining, by the data server,whether a trend associated with the operating element is indicatedaccording to the one or more moving averages and the volatility; andgenerating, by the data server, an alert signal in response to thedetermining that the trend is indicated.
 17. The method of claim 16,wherein the determining the one or more moving averages comprises:determining a first exponential moving average (EMA) for a first windowof the second portion of the condition indicator set; and determining asecond EMA for a second window of the second portion of the conditionindicator set, wherein the second window has a size smaller than a sizeof the first window; and wherein the determining whether the trend isindicated includes: generating an MACD value according to the first EMAand the second EMA; determining an adaptable trend threshold accordingto the volatility; and determining whether the trend is indicated inresponse to the MACD value being equal to, or exceeding, the adaptabletrend threshold.
 18. The method of claim 17, wherein the adaptable trendthreshold is the volatility divided by one of a scaling factorassociated with the size of the first window or a result of a functionapplied to the current condition indicator, and wherein the function isone of a step function, a linear function or a combination of linear andstep functions.
 19. The method of claim 17, wherein the determiningwhether the trend is indicated further comprises: determining that thetrend is not indicated in response to the MACD value being less than apredetermined level of the adaptable trend threshold, wherein thepredetermined level of the adaptable trend threshold is less than avalue of the adaptable trend threshold; and determining that the trendis indicated in response to the trend being indicated for a precedingcondition indicator and the MACD value being equal to, or greater than,the predetermined level of the adaptable trend threshold.
 20. The methodof claim 16, wherein each condition indicator in the second portion ofthe condition indicator set is prior to the current condition indicator;and wherein one or more calculation windows for the one or more movingaverages each lag a calculation window for the volatility by at leastone condition indicator.